Optimal control for sucrose crystallization with heuristic dynamic programming

This paper applies a neural-network-based approximate dynamic programming (ADP) method, namely, heuristic dynamic programming (HDP), to an industrial sucrose crystallization optimal control problem. The industrial sucrose crystallization is a nonlinear and slow time-varying process. It is quite difficult to establish a precise mechanism model of the crystallization, because of complex internal mechanism and interacting variables. We developed a neural network model of the crystallization based on the data from the actual sugar boiling process of sugar factory. HDP is a learning- and approximation-based approach which can solve the optimization control problem of nonlinear system. This paper covers the basic principle of this learning scheme and the design of neural network controller based on the approach. The result of simulation shows the controller based on heuristic dynamic programming approach can optimize industrial sucrose crystallization.